Snow cover variability at Polar Bear Pass, Nunavut
Bibliographic record
Abstract
Information on arctic snow covers is relevant for climate and hydrology studies and investigations into the sustainability of both arctic fauna and flora. This study aims to (1) highlight the variability of snow cover at Polar Bear Pass (PBP) at a range of scales: point, local, and regional using both in situ snow cover measurements and remote sensing imagery products; and (2) consider how snow cover at PBP might change in the future. Terrain-based snow surveys documented the end-of-winter snowpacks over several seasons (2008–2010, 2012–2013), and snowmelt was measured daily at typical terrain types. MODIS products (snow cover) were used to document spatial snow cover variability across PBP and Bathurst and Cornwallis Islands. Due to limited data, no significant difference in snow cover duration can be identified at PBP over the period of record. Locally, end-of-winter snow cover does vary across a range of terrain types with snow depths and densities reflecting polar oasis sites. Aspect remains a defining factor in terms of snow cover variability at PBP. Northern areas of the Pass melt earlier. Regionally, PBP tends to melt out earlier than most of Bathurst Island. In the future, we surmise that snowpacks at PBP will be thinner and disappear earlier.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".